2026 ASEE Annual Conference & Exposition

Ethical Use of Artificial Intelligence in Engineering Education: A Systematic Review

Presented at DSAI-Session 4: Ethics, Policy, and the AI-Integrated Engineering Workforce

The growing integration of generative artificial intelligence (AI) in engineering education has prompted increased attention to its role in teaching, learning, and assessment. As these tools become more widely adopted, scholarly discourse has shifted from questions of feasibility to concerns about the ethical, responsible, and pedagogically sound use of AI. This study presents a systematic review of the engineering education literature on how ethical guidance concerning AI use is articulated at institutional and instructional levels. Guided by PRISMA 2020 protocols, the review screened 2,158 articles published between 2000 and 2025 across eight databases. Of the 696 articles screened for full-text eligibility, 99 addressed ethical guidance for AI use in the engineering classroom. Inductive analysis of these articles revealed seven recurring themes: transparency and disclosure of AI use; faculty and student accountability and human oversight; student autonomy and agency; confidentiality and student data protection; academic integrity and authorship; fairness, equity, and bias mitigation; and beneficence, safety, and student wellbeing. Across these themes, integrity, accountability, and transparency were the most frequently emphasized aspects of ethical AI use, while beneficence and student autonomy received comparatively limited attention. Furthermore, findings indicate that ethical considerations in AI-enabled engineering education are fragmented and inconsistently articulated. Many guidelines were primarily student-facing and focused on academic integrity, disclosure, and misuse prevention, with less attention to reciprocal faculty accountability, institutional responsibility, and the broader pedagogical conditions needed for responsible AI integration. This imbalance is particularly consequential in engineering education, where instructional practices help prepare future engineers responsible for public safety, infrastructure reliability, and societal wellbeing. Overall, the findings highlight a critical gap between growing ethical awareness of AI risks and the absence of coherent, reciprocal ethical frameworks in engineering education practice. Addressing this gap will require institutions and accrediting bodies to adopt dynamic, human-centered frameworks that integrate ethical principles into pedagogy and assessment, strengthen ethical literacy among faculty and students, and promote shared human-AI accountability to support responsible, socially aligned AI integration in engineering education.
Keywords: Professional Responsibility, Institutional Policy, Classroom Practice, Equity and Access

Authors

Dr. Ibukun Samuel Osunbunmi http://orcid.org/https://0000-0002-7821-9059 The Pennsylvania State University [biography]

Ibukun Samuel Osunbunmi is an Assistant Research Professor, and Assessment and Instructional Specialist at Pennsylvania State University. He holds a Ph.D. degree in Engineering Education from Utah State University. Also, he has BSc and MSc degrees in mechanical engineering. His research interests include student engagement, design thinking, learning environment, evidence-based pedagogy, e-learning, broadening participation in STEM education, sustainable energy, and material characterization.
Deborah Moyaki http://orcid.org/0009-0005-7441-0306 The University of Georgia [biography]

Deborah Moyaki is a doctoral student in the Engineering Education and Transformative Practice program at the University of Georgia. She holds a bachelor’s degree in Educational Technology and is excited about the possibilities technology offers to the learning experience beyond the formal classroom setting. Her research focuses on improving the educational experience of engineering students using virtual reality labs and other emerging technologies.
VINCENT OLUWASETO FAKIYESI University of Georgia [biography]

Vincent Oluwaseto Fakiyesi is a doctoral student in the Engineering Education program at the University of Georgia. He holds a bachelor’s degree in Chemical Engineering and is interested in advancing assessment practices to better understand student learning. His research focuses on cognitive diagnostic modeling, student misconceptions, and adaptive assessment systems. He applies diagnostic models to analyze large-scale educational assessment data and generate actionable insights that support teaching, learning, and personalized feedback in engineering education. He also examines assessment practices in the era of generative artificial intelligence, emphasizing adaptive, interpretable, and learning-centered systems.
ISAAC DAMILARE DUNMOYE University of Georgia [biography]

Isaac Dunmoye
PhD in Engineering (in view), University of Georgia, USA,
M.Sc. in Electrical Engineering, University of Cape Town, South Africa, 2022.
B.Eng. in Agricultural and Biosystems Engineering, University of Ilorin, Nigeria, 2016.
Mary Ifeoma Nwanua University of Florida
MARY OLOYEDE Virginia Polytechnic Institute and State University
Bolaji Ruth Bamidele Utah State University [biography]

Bolaji Bamidele is currently pursuing her doctorate in the Department of Instructional Technology and Learning Sciences at Utah State University. She holds a BSc and an MSc in Sociology. Her research centers on identity, equity, informal science education, language socialization, and broadening participation in STEM education. Specifically, her work focuses on enhancing the participation and representation of Black girls in STEM by investigating science teaching and learning within counterspaces.
Taiwo Raphael Feyijimi http://orcid.org/0009-0009-5752-4175 University of Georgia [biography]

Taiwo is a highly skilled AI Engineer, Researcher, and Doctoral Student at the University of Georgia who completed his MS in Electrical and Computer Engineering in the College of Engineering. He is currently leveraging AI to tackle simple and longstanding problems in engineering education.

With over a decade of industry experience as a Technology Strategist and Technical Lead, he has established himself as a forward-thinking innovator in AI and EdTech.

His expertise spans Exploratory Data Analysis (EDA), Machine Learning (ML), Natural Language Processing (NLP), and Prompt Engineering Techniques (PETs) with Large Language Models (LLMs). Taiwo is known for his ability to collaborate effectively within and across organizations to meet project goals and drive transformative results. He excels in leading technical teams, offering strategic IT consultations, and implementing solutions that enhance productivity.
Dr. Nathaniel Hunsu The University of Georgia [biography]

Nathaniel Hunsu is an assistant professor of Engineering Education. He is affiliated with the Engineering Education Transformational Institute and the school of electrical and computer engineering at the university. His interest is at the nexus of the res

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 21, 2026, and to all visitors after the conference ends on June 24, 2026

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